Wiki says that you can prove this by using negative binomial series expansion.
Here is the way:
$$\sum_{m=0}^{n}\binom{m}{j}\binom{n-m}{k-j}=\sum_{m=j}^{n}\binom{m}{m-j}\binom{n-m}{n-m-k+j}$$$$=\sum_{m=j}^{n}\left(-1\right)^{m-j}\binom{-j-1}{m-j}\left(-1\right)^{n-m-k+j}\binom{-k+j-1}{n-m-k+j}$$
Setting $m-j \mapsto m$ yields:
$$=\left(-1\right)^{n-k}\sum_{m=0}^{n-j}\binom{-j-1}{m}\binom{-k+j-1}{n-m-k}$$$$=\left(-1\right)^{n-k}\binom{-k-2}{n-k}$$$$=\binom{n+1}{n-k}=\binom{n+1}{k+1}$$
So we showed that :
$$\bbox[5px,border:2px solid #00A000]{\sum_{m=0}^{n}\binom{m}{j}\binom{n-m}{k-j}=\binom{n+1}{k+1}}$$
Which is the claim.
Note:
Here Vandermonde's convolution is generalized:
$$\large\sum_{k=0}^{\large\sum_{\mu=1}^{i}n_\mu}\binom{\large\sum_{\mu=1}^{i}n_\mu}{k}x^{k}=\left(1+x\right)^{\large\sum_{\mu=1}^{i}n_\mu}$$$$=\large\prod_{\mu=1}^{ i}\left(1+x\right)^{n_\mu}=\prod_{\mu=1}^{ i}\sum_{k_{\mu }=0}^{n_{\mu }}\binom{n_\mu}{k_\mu}x^{k_\mu}$$$$=\large{\sum_{k=0}^{\large\sum_{\mu=1}^{i}n_\mu }\sum_{\sum_{\mu=1}^{i} v_\mu=k}^{ }}\;\;{\prod_{\mu=1}^{ i}}a_{v_\mu}\large x^{k}$$
Where: $$\large a_{v_\mu}=\binom{n_\mu}{v_\mu}$$
Comparing the coefficient of $x^k$ on the both sides follows:
$$\bbox[5px,border:2px solid #00A000]{\binom{\large\sum_{\mu=1}^{i}n_\mu}{k}=\large\sum_{\sum_{\mu=1}^{i}v_{\mu}=k}^{ }\;\;\prod_{\mu=1}^{i}\large \binom{n_\mu}{v_\mu}}$$
Regular Vandermonde's convolution is a special case of this relation which can be derived by setting $i \mapsto 2$